There is a need to develop effective curricula based on real-life cases that can be used to broaden students’ participation and motivate them during their studies. Real-life case examples are crucial to justify the learning of the respective discipline. In our study, we explore the application of image processing using Python in the context of processing ancient manuscripts, particularly palimpsests. Palimpsests are invaluable historical manuscripts, often rewritten and reused over time. They pose challenges for researchers due to legibility and interpretation issues. Python and AI can help decipher them. This interdisciplinary study not only introduces students to applied AI techniques, but also enriches their practice with the use of various image preprocessing techniques to enhance the readability of palimpsests. Specific methods such as Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gaussian smoothing are detailed to demonstrate various preprocessing strategies. Current educational trends in Applied Artificial Intelligence (AAI) are discussed in this paper. Experimental techniques are elucidated through example code implementations showing the practical application of these technologies. The research contributes to the advancement of both educational and scientific applications by introducing learners to the history of their country through the analysis of ancient manuscripts. By engaging with these materials, students gain practical skills in AI while deepening their historical knowledge, fostering a holistic and interdisciplinary approach to learning.

The palimpsests represent unique historical sources that hold the potential for new insights in the field of human history. These manuscripts, rewritten and reused over time, pose challenges in the research related to their readability and interpretation. The present study aims to investigate the readability of palimpsests through the use of image preprocessing techniques. The article focuses on methods for the preprocessing of palimpsests that could lead to a significant improvement in the readability of the ‘hidden’ text. The challenges encountered during the processing of palimpsests are explored and various techniques applicable to improving the readability of these manuscripts are analyzed.

The primary goal of preprocessing in this context is to separate the ‘hidden’ text from the visible one, neutralizing material defects and aging. The article presents specific methods such as extracting specific color range from an palimsest image. The experimental techniques are highlighted with sample codes illustrating the application of the respective technology. The current research attempts to advance the development of methods for processing palimpsests and opens up new perspectives for extracting information from those historically valuable manuscripts.